*Published: 2012-08-26*

# Petersburg Paradox With R

I've been flirting with the idea of learning to use R for awhile now. Previously I have had Stata available to me and since I learned to use it pretty well in grad school (round 1), I just couldn't motivate myself to learn R.

Well all that has changed now. I no longer have easy access to Stata, so R it is! I've been reading through a book I've had for some time (I have the first edition) and it has been a good read.

But you can't learn something just by reading, at some point you have to do stuff. So I wrote a couple functions to simulate the Petersburg Paradox, which I find rather interesting.

The Petersburg Paradox describes a simple gambling game (though not a very smart one for the house). Here is the description from Wikipedia:

A casino offers a game of chance for a single player in which a fair coin is tossed at each stage. The pot starts at 1 dollar and is doubled every time a head appears. The first time a tail appears, the game ends and the player wins whatever is in the pot. Thus the player wins 1 dollar if a tail appears on the first toss, 2 dollars if a head appears on the first toss and a tail on the second, 4 dollars if a head appears on the first two tosses and a tail on the third, 8 dollars if a head appears on the first three tosses and a tail on the fourth, and so on. In short, the player wins 2^(k-1) dollars if the coin is tossed k times until the first tail appears.

Here's the code to simulate one or more games.

There are three functions. The first, `pburg`

, plays a single game and returns
the winnings. The next, `ipburg`

, plays `n`

games and returns a vector of
the winnings for each round. The final function, `cumpburg`

, returns the
cumulative average of the winnings for `n`

games. If `v`

is specified then
only every `v`

cumulative observations are kept (to make the data plot more
nicely). Here is a sample invocation:

```
plot(seq(1,20000,100), cumpburg(20000,100), xlab="Trial #", ylab="# of
Flips", type="l")
```

So, what do the results look like? Here's an example (if you compare it against the graph on Wikipedia you'll see they are similar looking).